Forecast-based stochastic optimization for a load powered by wave energy
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Dillon, Trent (ORCID:0000000201036449)↗
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Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System
Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.
Sana Khan↗
Usefulness of ensemble forecasts from NCEP Climate Forecast System in sub‐seasonal to intra‐annual forecasting
Explore the source record for details and available documents.
Sanjiv Kumar↗
Wind Energy Forecasting with the Weather Research and Forecasting Model
This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.
17 WIND ENERGY↗
Forecasting forecast skill
It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.
Kalnay, Eugenia↗
Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System
The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.
Veronica Ruiz Xomchuk↗
Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain Corrected Satellite Sea Surface Salinity Using the GMAO S2S Forecast System
During the past years, we have seen that the La Nina to El Nino transition has had a significant global socio-economic impact and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite Sea Surface Temperature (SST) aids in constraining surface heat-fluxes, leading to improved subseasonal to seasonal forecasts of the coupled system. However, few studies have focused on improving the fresh-water flux and near-surface density and mixing through assimilation of satellite sea surface salinity (SSS). For expediency, the few ocean models that do assimilate SSS bias-correct the satellite SSS data to normalize towards the near-surface Argo data. However, in rainy regions, where buoyant water sits as a fresh lens at the surface, this assumption is likely inadequate. In previous work, we have shown that adjusting SSS data to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 has improved the near-surface density and mixed layer depth, leading to deeper thermocline and improved the NINO3.4 SST forecasts. Now we utilize the Soil Moisture/Ocean Salinity, Rain Corrected (SMOS_RC) SSS product provided by the Centre Aval de Traitement des données SMOS (CATDS CPDC) to represent the Sb more accurately at first model layer (in our case 5 m). Rather than using a diffusivity model as with RIM, SMOS_RC relies on an observed relationship between the spatial heterogeneity of SMOS SSS and instantaneous rain rate (RR) (Supply et al., 2020). In order to test the impact of SMOS_RC versus SMOS, we compare two reanalyses over the period 2014 to 2021. For both reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is (i.e., with the fresh bias), and a separate reanalysis is performed assimilating the SMOS_RC data. We assess the impact for near-surface and subsurface dynamics within ocean reanalyses by validating against observations and explore how SSS assimilation (SMOS versus SMOS_RC) impacts dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (GEOS S2S-3, Molod et al., 2020, Hackert et al., 2023). We will show that improved SSS estimates and near-surface density and mixing led to more accurate coupled air/sea interaction and better ENSO forecasts.
Eric Hackert↗
Improving High-Resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with an Updated Kain-Fritsch Scheme
Efforts to improve the prediction accuracy of high resolution (1-10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale-awareness for improving high-resolution short-term precipitation forecasts in the WRF model. To address these issues, we introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain-Fritsch (KF) convective parameterization scheme in the WRF model. These changes include subgrid-scale cloud-radiation interactions, a dynamic adjustment timescale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level-based entrainment methodology that includes scale dependency. A series of 48-hour retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that (1) the source of initial conditions play a key role in high-resolution precipitation forecasting, and (2) our updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, we found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.
convective parameterization↗
A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs
Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.
Srinivasan, Shriram↗
AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System
A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.
Reale, O.↗
Global-to-local air quality forecasts using the NASA GEOS Composition Forecast System
Since 2019, the NASA Global Earth Observing System (GEOS) model has been used to generate global, near-real-time estimates and daily five-day forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). Because GEOS-CF includes atmospheric levels up through the stratosphere, this system has been leveraged to support the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite mission and provide stratospheric intrusion alerts to ground-based monitoring stations. We will present recent advances to GEOS-CF which target increased computational efficiency and accuracy. These include the incorporation of simplified chemistry mechanisms to accelerate model forecasts, use of model-observation data fusion techniques to provide highly localized forecasts, and assimilation of satellite observations to produce more accurate model analyses. We further discuss our attempts to make these tools publicly available on platforms outside the NASA domain, such as Google Earth Engine and Amazon Web Services with the goal to facilitate the integration of state-of-the-science air quality information onto platforms used by stakeholders, air quality managers, and the public.
Emma Knowland↗
Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement
Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.
distributed energy resources↗
A simple Lagrangian forecast system with aviation forecast potential
A trajectory forecast procedure is developed which uses geopotential tendency fields obtained from a simple, multiple layer, potential vorticity conservative isentropic model. This model can objectively account for short-term advective changes in the mass field when combined with fine-scale initial analyses. This procedure for producing short-term, upper-tropospheric trajectory forecasts employs a combination of a detailed objective analysis technique, an efficient mass advection model, and a diagnostically proven trajectory algorithm, none of which require extensive computer resources. Results of initial tests are presented, which indicate an exceptionally good agreement for trajectory paths entering the jet stream and passing through an intensifying trough. It is concluded that this technique not only has potential for aiding in route determination, fuel use estimation, and clear air turbulence detection, but also provides an example of the types of short range forecasting procedures which can be applied at local forecast centers using simple algorithms and a minimum of computer resources.
Petersen, R. A.↗
Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa
Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72‐hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.
Kabuchanga, Eric↗
Grand Valley Ecological Forecasting II: Forecasting Trends in Pinyon-Juniper and Sagebrush Habitat Relative to Wildfire, Drought, Beetle Disturbance, and Treatment Impact for Management Planning
Disturbances and landcover change in pinyon-juniper and sagebrush ecosystems are exacerbated by environmental conditions such as variability in climate characteristics. Our DEVELOP team partnered with the National Park Service (NPS) in Colorado National Monument and the Bureau of Land Management (BLM)in McInnis Canyons and Dominguez-Escalante National Conservation Areas to investigate disturbances to land cover. NPS partners were interested in identifying areas at risk of pinyon-juniper die-off or encroachment by invasive species. The BLM partners prioritized identifying areas suitable for fire reduction/prevention treatment. To address these concerns, we forecasted landcover change in the Grand Valley region of Colorado using NASA Earth observation data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua. We collected and analyzed these data in conjunction with Term I of this project. We found that the primary drivers of forecasted landcover change in the study area were aspect and elevation. Our forecasted landcover change maps, created using the Idrisi TerrSet Land Change Modeler, addressed the needs of our partners by showing potential habitat suitability trends, which will inform management planning. Forecasted landcover maps indicated that by 2040, ecosystems within partner management areas will likely see tree encroachment on shrublands.
William Curtiss↗
Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts
Not provided.
Energy & Fuels↗
Online updating of a Markovian solar forecast representation
Not Available
Sward, Jeffrey A. (ORCID:0000000217438944)↗
AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System
Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.
Reale, O.↗